Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations7728394
Missing cells1611702
Missing cells (%)0.9%
Duplicate rows153572
Duplicate rows (%)2.0%
Total size in memory832.9 MiB
Average record size in memory113.0 B

Variable types

Categorical3
Numeric7
Text3
Boolean9

Alerts

Dataset has 153572 (2.0%) duplicate rowsDuplicates
Severity is highly imbalanced (55.4%) Imbalance
Amenity is highly imbalanced (90.3%) Imbalance
Give_Way is highly imbalanced (95.7%) Imbalance
Junction is highly imbalanced (62.0%) Imbalance
No_Exit is highly imbalanced (97.5%) Imbalance
Railway is highly imbalanced (92.8%) Imbalance
Stop is highly imbalanced (81.7%) Imbalance
Traffic_Calming is highly imbalanced (98.9%) Imbalance
Temperature(F) has 163853 (2.1%) missing values Missing
Humidity(%) has 174144 (2.3%) missing values Missing
Pressure(in) has 140679 (1.8%) missing values Missing
Visibility(mi) has 177098 (2.3%) missing values Missing
Wind_Direction has 175206 (2.3%) missing values Missing
Wind_Speed(mph) has 571233 (7.4%) missing values Missing
Weather_Condition has 173459 (2.2%) missing values Missing
Distance(mi) is highly skewed (γ1 = 20.38575876) Skewed
Duration_Seconds is highly skewed (γ1 = 50.63729121) Skewed
Distance(mi) has 3302161 (42.7%) zeros Zeros
Wind_Speed(mph) has 961643 (12.4%) zeros Zeros

Reproduction

Analysis started2024-11-04 23:29:24.880102
Analysis finished2024-11-04 23:33:17.018279
Duration3 minutes and 52.14 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Severity
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.0 MiB
2
6156981 
3
1299337 
4
 
204710
1
 
67366

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7728394
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 6156981
79.7%
3 1299337
 
16.8%
4 204710
 
2.6%
1 67366
 
0.9%

Length

2024-11-05T00:33:17.094980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T00:33:17.164011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 6156981
79.7%
3 1299337
 
16.8%
4 204710
 
2.6%
1 67366
 
0.9%

Most occurring characters

ValueCountFrequency (%)
2 6156981
79.7%
3 1299337
 
16.8%
4 204710
 
2.6%
1 67366
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7728394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 6156981
79.7%
3 1299337
 
16.8%
4 204710
 
2.6%
1 67366
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7728394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 6156981
79.7%
3 1299337
 
16.8%
4 204710
 
2.6%
1 67366
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7728394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 6156981
79.7%
3 1299337
 
16.8%
4 204710
 
2.6%
1 67366
 
0.9%

Distance(mi)
Real number (ℝ)

Skewed  Zeros 

Distinct22382
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56184228
Minimum0
Maximum441.75
Zeros3302161
Zeros (%)42.7%
Negative0
Negative (%)0.0%
Memory size59.0 MiB
2024-11-05T00:33:17.230699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.03
Q30.464
95-th percentile2.67
Maximum441.75
Range441.75
Interquartile range (IQR)0.464

Descriptive statistics

Standard deviation1.7768106
Coefficient of variation (CV)3.1624722
Kurtosis1649.5954
Mean0.56184228
Median Absolute Deviation (MAD)0.03
Skewness20.385759
Sum4342138.5
Variance3.1570559
MonotonicityNot monotonic
2024-11-05T00:33:17.306997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3302161
42.7%
0.01 262493
 
3.4%
0.008 14558
 
0.2%
0.009 13836
 
0.2%
0.009999999776 13367
 
0.2%
0.007 12413
 
0.2%
0.011 11625
 
0.2%
0.03 11322
 
0.1%
0.024 11002
 
0.1%
0.028 10927
 
0.1%
Other values (22372) 4064690
52.6%
ValueCountFrequency (%)
0 3302161
42.7%
0.001 5585
 
0.1%
0.002 3078
 
< 0.1%
0.003 4263
 
0.1%
0.004 6337
 
0.1%
0.005 8253
 
0.1%
0.006 10121
 
0.1%
0.007 12413
 
0.2%
0.008 14558
 
0.2%
0.009 13836
 
0.2%
ValueCountFrequency (%)
441.75 1
< 0.1%
336.5700073 1
< 0.1%
333.6300049 1
< 0.1%
254.3999939 1
< 0.1%
251.2200012 1
< 0.1%
242.3399963 1
< 0.1%
227.2100067 1
< 0.1%
224.5899963 1
< 0.1%
210.0800018 1
< 0.1%
194.7299957 1
< 0.1%

Street
Text

Distinct336306
Distinct (%)4.4%
Missing10869
Missing (%)0.1%
Memory size59.0 MiB
2024-11-05T00:33:17.610569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length59
Median length47
Mean length11.062818
Min length1

Characters and Unicode

Total characters85377573
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129934 ?
Unique (%)1.7%

Sample

1st rowI-70 E
2nd rowBrice Rd
3rd rowState Route 32
4th rowI-75 S
5th rowMiamisburg Centerville Rd
ValueCountFrequency (%)
n 1199552
 
6.3%
s 1194304
 
6.2%
rd 1159617
 
6.0%
w 941905
 
4.9%
e 931753
 
4.9%
st 684198
 
3.6%
ave 640512
 
3.3%
blvd 343950
 
1.8%
fwy 330007
 
1.7%
dr 327207
 
1.7%
Other values (74656) 11416775
59.6%
2024-11-05T00:33:17.897437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13148775
 
15.4%
e 4752092
 
5.6%
a 3818802
 
4.5%
r 3231482
 
3.8%
t 3213194
 
3.8%
o 2990987
 
3.5%
S 2982408
 
3.5%
n 2968805
 
3.5%
d 2778142
 
3.3%
l 2764944
 
3.2%
Other values (70) 42727942
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85377573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13148775
 
15.4%
e 4752092
 
5.6%
a 3818802
 
4.5%
r 3231482
 
3.8%
t 3213194
 
3.8%
o 2990987
 
3.5%
S 2982408
 
3.5%
n 2968805
 
3.5%
d 2778142
 
3.3%
l 2764944
 
3.2%
Other values (70) 42727942
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85377573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13148775
 
15.4%
e 4752092
 
5.6%
a 3818802
 
4.5%
r 3231482
 
3.8%
t 3213194
 
3.8%
o 2990987
 
3.5%
S 2982408
 
3.5%
n 2968805
 
3.5%
d 2778142
 
3.3%
l 2764944
 
3.2%
Other values (70) 42727942
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85377573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13148775
 
15.4%
e 4752092
 
5.6%
a 3818802
 
4.5%
r 3231482
 
3.8%
t 3213194
 
3.8%
o 2990987
 
3.5%
S 2982408
 
3.5%
n 2968805
 
3.5%
d 2778142
 
3.3%
l 2764944
 
3.2%
Other values (70) 42727942
50.0%

Zipcode
Text

Distinct825094
Distinct (%)10.7%
Missing1915
Missing (%)< 0.1%
Memory size59.0 MiB
2024-11-05T00:33:18.454900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length6.4678536
Min length5

Characters and Unicode

Total characters49973735
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique450606 ?
Unique (%)5.8%

Sample

1st row45424
2nd row43068-3402
3rd row45176
4th row45417
5th row45459
ValueCountFrequency (%)
91761 11247
 
0.1%
91706 10022
 
0.1%
92407 8922
 
0.1%
92507 8850
 
0.1%
33186 8375
 
0.1%
32819 7461
 
0.1%
91765 7377
 
0.1%
33169 7106
 
0.1%
90023 7066
 
0.1%
92324 7010
 
0.1%
Other values (825084) 7643043
98.9%
2024-11-05T00:33:18.970742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6303792
12.6%
2 6159301
12.3%
3 5704889
11.4%
1 5682170
11.4%
9 4463210
8.9%
7 4316166
8.6%
5 4183724
8.4%
4 4137052
8.3%
6 3427226
6.9%
8 3321903
6.6%
Other values (3) 2274302
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49973735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6303792
12.6%
2 6159301
12.3%
3 5704889
11.4%
1 5682170
11.4%
9 4463210
8.9%
7 4316166
8.6%
5 4183724
8.4%
4 4137052
8.3%
6 3427226
6.9%
8 3321903
6.6%
Other values (3) 2274302
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49973735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6303792
12.6%
2 6159301
12.3%
3 5704889
11.4%
1 5682170
11.4%
9 4463210
8.9%
7 4316166
8.6%
5 4183724
8.4%
4 4137052
8.3%
6 3427226
6.9%
8 3321903
6.6%
Other values (3) 2274302
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49973735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6303792
12.6%
2 6159301
12.3%
3 5704889
11.4%
1 5682170
11.4%
9 4463210
8.9%
7 4316166
8.6%
5 4183724
8.4%
4 4137052
8.3%
6 3427226
6.9%
8 3321903
6.6%
Other values (3) 2274302
 
4.6%

Temperature(F)
Real number (ℝ)

Missing 

Distinct860
Distinct (%)< 0.1%
Missing163853
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean61.663286
Minimum-89
Maximum207
Zeros2775
Zeros (%)< 0.1%
Negative19478
Negative (%)0.3%
Memory size59.0 MiB
2024-11-05T00:33:19.048966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-89
5-th percentile28
Q149
median64
Q376
95-th percentile89
Maximum207
Range296
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.013653
Coefficient of variation (CV)0.30834642
Kurtosis-0.0012691043
Mean61.663286
Median Absolute Deviation (MAD)13
Skewness-0.513733
Sum4.6645445 × 108
Variance361.51901
MonotonicityNot monotonic
2024-11-05T00:33:19.130299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 170991
 
2.2%
73 170898
 
2.2%
68 163767
 
2.1%
72 160498
 
2.1%
75 158448
 
2.1%
70 155568
 
2.0%
63 149787
 
1.9%
59 149017
 
1.9%
64 148466
 
1.9%
79 147140
 
1.9%
Other values (850) 5989961
77.5%
(Missing) 163853
 
2.1%
ValueCountFrequency (%)
-89 10
< 0.1%
-77.8 11
< 0.1%
-58 1
 
< 0.1%
-50 1
 
< 0.1%
-45 1
 
< 0.1%
-44 1
 
< 0.1%
-40 2
 
< 0.1%
-38 3
 
< 0.1%
-37 5
< 0.1%
-36 4
 
< 0.1%
ValueCountFrequency (%)
207 3
< 0.1%
203 1
 
< 0.1%
196 5
< 0.1%
189 1
 
< 0.1%
174 2
 
< 0.1%
172 2
 
< 0.1%
170.6 1
 
< 0.1%
168.8 1
 
< 0.1%
167 1
 
< 0.1%
162 2
 
< 0.1%

Humidity(%)
Real number (ℝ)

Missing 

Distinct100
Distinct (%)< 0.1%
Missing174144
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean64.831041
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.0 MiB
2024-11-05T00:33:19.200264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24
Q148
median67
Q384
95-th percentile97
Maximum100
Range99
Interquartile range (IQR)36

Descriptive statistics

Standard deviation22.820968
Coefficient of variation (CV)0.3520068
Kurtosis-0.7234553
Mean64.831041
Median Absolute Deviation (MAD)18
Skewness-0.39484242
Sum4.897499 × 108
Variance520.79656
MonotonicityNot monotonic
2024-11-05T00:33:19.278561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 290345
 
3.8%
100 286680
 
3.7%
87 169582
 
2.2%
90 166492
 
2.2%
89 140593
 
1.8%
96 134809
 
1.7%
84 126652
 
1.6%
81 126612
 
1.6%
82 124386
 
1.6%
86 121255
 
1.6%
Other values (90) 5866844
75.9%
(Missing) 174144
 
2.3%
ValueCountFrequency (%)
1 49
 
< 0.1%
2 189
 
< 0.1%
3 670
 
< 0.1%
4 2167
 
< 0.1%
5 4113
 
0.1%
6 6010
0.1%
7 8072
0.1%
8 9661
0.1%
9 11116
0.1%
10 13495
0.2%
ValueCountFrequency (%)
100 286680
3.7%
99 14262
 
0.2%
98 6977
 
0.1%
97 88156
 
1.1%
96 134809
1.7%
95 9612
 
0.1%
94 119009
1.5%
93 290345
3.8%
92 66899
 
0.9%
91 37561
 
0.5%

Pressure(in)
Real number (ℝ)

Missing 

Distinct1144
Distinct (%)< 0.1%
Missing140679
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean29.538986
Minimum0
Maximum58.63
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size59.0 MiB
2024-11-05T00:33:19.346405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.98
Q129.37
median29.86
Q330.03
95-th percentile30.26
Maximum58.63
Range58.63
Interquartile range (IQR)0.66

Descriptive statistics

Standard deviation1.0061898
Coefficient of variation (CV)0.034063113
Kurtosis21.841661
Mean29.538986
Median Absolute Deviation (MAD)0.24
Skewness-3.6387719
Sum2.241334 × 108
Variance1.0124179
MonotonicityNot monotonic
2024-11-05T00:33:19.418054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.96 123289
 
1.6%
29.99 121836
 
1.6%
30.01 119735
 
1.5%
29.94 119000
 
1.5%
30.04 113905
 
1.5%
29.97 112320
 
1.5%
30.03 110898
 
1.4%
29.91 110700
 
1.4%
30 109999
 
1.4%
29.95 109924
 
1.4%
Other values (1134) 6436109
83.3%
(Missing) 140679
 
1.8%
ValueCountFrequency (%)
0 3
 
< 0.1%
0.02 1
 
< 0.1%
0.12 1
 
< 0.1%
0.29 2
 
< 0.1%
0.3 6
< 0.1%
0.39 1
 
< 0.1%
2.98 1
 
< 0.1%
2.99 9
< 0.1%
3 2
 
< 0.1%
3.01 2
 
< 0.1%
ValueCountFrequency (%)
58.63 9
< 0.1%
58.39 2
 
< 0.1%
58.32 1
 
< 0.1%
58.13 1
 
< 0.1%
58.1 4
< 0.1%
58.04 3
 
< 0.1%
58.03 1
 
< 0.1%
57.74 1
 
< 0.1%
57.54 2
 
< 0.1%
56.54 2
 
< 0.1%

Visibility(mi)
Real number (ℝ)

Missing 

Distinct92
Distinct (%)< 0.1%
Missing177098
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean9.0903764
Minimum0
Maximum140
Zeros7679
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size59.0 MiB
2024-11-05T00:33:19.490787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.5
Q110
median10
Q310
95-th percentile10
Maximum140
Range140
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6883159
Coefficient of variation (CV)0.29573208
Kurtosis81.893919
Mean9.0903764
Median Absolute Deviation (MAD)0
Skewness2.3166628
Sum68644123
Variance7.2270425
MonotonicityNot monotonic
2024-11-05T00:33:19.563819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 6070231
78.5%
7 217027
 
2.8%
9 188529
 
2.4%
8 149975
 
1.9%
5 144153
 
1.9%
6 126586
 
1.6%
2 121785
 
1.6%
4 119770
 
1.5%
3 117493
 
1.5%
1 102557
 
1.3%
Other values (82) 193190
 
2.5%
(Missing) 177098
 
2.3%
ValueCountFrequency (%)
0 7679
 
0.1%
0.06 323
 
< 0.1%
0.1 1287
 
< 0.1%
0.12 1775
 
< 0.1%
0.19 41
 
< 0.1%
0.2 12105
0.2%
0.25 27344
0.4%
0.31 4
 
< 0.1%
0.38 337
 
< 0.1%
0.4 98
 
< 0.1%
ValueCountFrequency (%)
140 3
 
< 0.1%
130 1
 
< 0.1%
120 5
 
< 0.1%
111 3
 
< 0.1%
110 1
 
< 0.1%
105 1
 
< 0.1%
101 1
 
< 0.1%
100 47
< 0.1%
98 1
 
< 0.1%
90 13
 
< 0.1%

Wind_Direction
Categorical

Missing 

Distinct24
Distinct (%)< 0.1%
Missing175206
Missing (%)2.3%
Memory size59.0 MiB
CALM
961624 
S
 
419989
SSW
 
384840
W
 
383913
WNW
 
378781
Other values (19)
5024041 

Length

Max length8
Median length5
Mean length2.8361859
Min length1

Characters and Unicode

Total characters21422245
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCalm
2nd rowCalm
3rd rowSW
4th rowSW
5th rowSW

Common Values

ValueCountFrequency (%)
CALM 961624
 
12.4%
S 419989
 
5.4%
SSW 384840
 
5.0%
W 383913
 
5.0%
WNW 378781
 
4.9%
NW 369352
 
4.8%
Calm 368557
 
4.8%
SW 364470
 
4.7%
WSW 353806
 
4.6%
SSE 349110
 
4.5%
Other values (14) 3218746
41.6%

Length

2024-11-05T00:33:19.636206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
calm 1330181
17.6%
s 419989
 
5.6%
ssw 384840
 
5.1%
w 383913
 
5.1%
wnw 378781
 
5.0%
nw 369352
 
4.9%
sw 364470
 
4.8%
wsw 353806
 
4.7%
sse 349110
 
4.6%
nnw 333427
 
4.4%
Other values (13) 2885319
38.2%

Most occurring characters

ValueCountFrequency (%)
W 3465927
16.2%
S 3346752
15.6%
N 2903258
13.6%
E 2593990
12.1%
C 1330181
 
6.2%
A 1212190
 
5.7%
L 961624
 
4.5%
M 961624
 
4.5%
a 700094
 
3.3%
t 599056
 
2.8%
Other values (12) 3347549
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21422245
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 3465927
16.2%
S 3346752
15.6%
N 2903258
13.6%
E 2593990
12.1%
C 1330181
 
6.2%
A 1212190
 
5.7%
L 961624
 
4.5%
M 961624
 
4.5%
a 700094
 
3.3%
t 599056
 
2.8%
Other values (12) 3347549
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21422245
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 3465927
16.2%
S 3346752
15.6%
N 2903258
13.6%
E 2593990
12.1%
C 1330181
 
6.2%
A 1212190
 
5.7%
L 961624
 
4.5%
M 961624
 
4.5%
a 700094
 
3.3%
t 599056
 
2.8%
Other values (12) 3347549
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21422245
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 3465927
16.2%
S 3346752
15.6%
N 2903258
13.6%
E 2593990
12.1%
C 1330181
 
6.2%
A 1212190
 
5.7%
L 961624
 
4.5%
M 961624
 
4.5%
a 700094
 
3.3%
t 599056
 
2.8%
Other values (12) 3347549
15.6%

Wind_Speed(mph)
Real number (ℝ)

Missing  Zeros 

Distinct184
Distinct (%)< 0.1%
Missing571233
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean7.6854896
Minimum0
Maximum1087
Zeros961643
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size59.0 MiB
2024-11-05T00:33:19.703390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6
median7
Q310.4
95-th percentile17
Maximum1087
Range1087
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation5.4249834
Coefficient of variation (CV)0.7058735
Kurtosis1085.4752
Mean7.6854896
Median Absolute Deviation (MAD)3
Skewness8.0494761
Sum55006286
Variance29.430445
MonotonicityNot monotonic
2024-11-05T00:33:19.776041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 961643
 
12.4%
5 534875
 
6.9%
6 517199
 
6.7%
3 514123
 
6.7%
7 480904
 
6.2%
8 432522
 
5.6%
9 389161
 
5.0%
10 324080
 
4.2%
12 280269
 
3.6%
4.6 217615
 
2.8%
Other values (174) 2504770
32.4%
(Missing) 571233
 
7.4%
ValueCountFrequency (%)
0 961643
12.4%
1 195
 
< 0.1%
1.2 445
 
< 0.1%
2 451
 
< 0.1%
2.3 906
 
< 0.1%
3 514123
6.7%
3.5 203579
 
2.6%
4.6 217615
 
2.8%
5 534875
6.9%
5.8 216150
 
2.8%
ValueCountFrequency (%)
1087 1
 
< 0.1%
984 1
 
< 0.1%
822.8 7
< 0.1%
812 1
 
< 0.1%
703.1 2
 
< 0.1%
580 2
 
< 0.1%
518 2
 
< 0.1%
471.8 1
 
< 0.1%
328 1
 
< 0.1%
255 1
 
< 0.1%

Weather_Condition
Text

Missing 

Distinct144
Distinct (%)< 0.1%
Missing173459
Missing (%)2.2%
Memory size59.0 MiB
2024-11-05T00:33:19.876126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length7.6729617
Min length3

Characters and Unicode

Total characters57968727
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowLight Rain
2nd rowLight Rain
3rd rowOvercast
4th rowMostly Cloudy
5th rowMostly Cloudy
ValueCountFrequency (%)
fair 2596473
24.8%
cloudy 2576033
24.6%
mostly 1032703
 
9.9%
clear 808743
 
7.7%
partly 709213
 
6.8%
light 545023
 
5.2%
rain 509071
 
4.9%
overcast 382866
 
3.7%
scattered 204829
 
2.0%
clouds 204829
 
2.0%
Other values (51) 888209
 
8.5%
2024-11-05T00:33:20.036089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 5372144
 
9.3%
a 5364570
 
9.3%
r 4858585
 
8.4%
y 4510118
 
7.8%
o 4153193
 
7.2%
i 3924138
 
6.8%
C 3589627
 
6.2%
t 3202474
 
5.5%
d 3164721
 
5.5%
2903057
 
5.0%
Other values (36) 16926100
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57968727
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 5372144
 
9.3%
a 5364570
 
9.3%
r 4858585
 
8.4%
y 4510118
 
7.8%
o 4153193
 
7.2%
i 3924138
 
6.8%
C 3589627
 
6.2%
t 3202474
 
5.5%
d 3164721
 
5.5%
2903057
 
5.0%
Other values (36) 16926100
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57968727
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 5372144
 
9.3%
a 5364570
 
9.3%
r 4858585
 
8.4%
y 4510118
 
7.8%
o 4153193
 
7.2%
i 3924138
 
6.8%
C 3589627
 
6.2%
t 3202474
 
5.5%
d 3164721
 
5.5%
2903057
 
5.0%
Other values (36) 16926100
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57968727
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 5372144
 
9.3%
a 5364570
 
9.3%
r 4858585
 
8.4%
y 4510118
 
7.8%
o 4153193
 
7.2%
i 3924138
 
6.8%
C 3589627
 
6.2%
t 3202474
 
5.5%
d 3164721
 
5.5%
2903057
 
5.0%
Other values (36) 16926100
29.2%

Amenity
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7632060 
True
 
96334
ValueCountFrequency (%)
False 7632060
98.8%
True 96334
 
1.2%
2024-11-05T00:33:20.097244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Crossing
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
6854631 
True
873763 
ValueCountFrequency (%)
False 6854631
88.7%
True 873763
 
11.3%
2024-11-05T00:33:20.146587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Give_Way
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7691812 
True
 
36582
ValueCountFrequency (%)
False 7691812
99.5%
True 36582
 
0.5%
2024-11-05T00:33:20.194325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Junction
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7157052 
True
 
571342
ValueCountFrequency (%)
False 7157052
92.6%
True 571342
 
7.4%
2024-11-05T00:33:20.236027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

No_Exit
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7708849 
True
 
19545
ValueCountFrequency (%)
False 7708849
99.7%
True 19545
 
0.3%
2024-11-05T00:33:20.290104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Railway
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7661415 
True
 
66979
ValueCountFrequency (%)
False 7661415
99.1%
True 66979
 
0.9%
2024-11-05T00:33:20.336538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Stop
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7514023 
True
 
214371
ValueCountFrequency (%)
False 7514023
97.2%
True 214371
 
2.8%
2024-11-05T00:33:20.382241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Traffic_Calming
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
7720796 
True
 
7598
ValueCountFrequency (%)
False 7720796
99.9%
True 7598
 
0.1%
2024-11-05T00:33:20.432611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Traffic_Signal
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
False
6584622 
True
1143772 
ValueCountFrequency (%)
False 6584622
85.2%
True 1143772
 
14.8%
2024-11-05T00:33:20.479055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Civil_Twilight
Categorical

Distinct2
Distinct (%)< 0.1%
Missing23246
Missing (%)0.3%
Memory size59.0 MiB
Day
5695619 
Night
2009529 

Length

Max length5
Median length3
Mean length3.5216069
Min length3

Characters and Unicode

Total characters27134502
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight
2nd rowNight
3rd rowNight
4th rowDay
5th rowDay

Common Values

ValueCountFrequency (%)
Day 5695619
73.7%
Night 2009529
 
26.0%
(Missing) 23246
 
0.3%

Length

2024-11-05T00:33:20.539015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T00:33:20.599686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
day 5695619
73.9%
night 2009529
 
26.1%

Most occurring characters

ValueCountFrequency (%)
D 5695619
21.0%
a 5695619
21.0%
y 5695619
21.0%
N 2009529
 
7.4%
i 2009529
 
7.4%
g 2009529
 
7.4%
h 2009529
 
7.4%
t 2009529
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27134502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 5695619
21.0%
a 5695619
21.0%
y 5695619
21.0%
N 2009529
 
7.4%
i 2009529
 
7.4%
g 2009529
 
7.4%
h 2009529
 
7.4%
t 2009529
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27134502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 5695619
21.0%
a 5695619
21.0%
y 5695619
21.0%
N 2009529
 
7.4%
i 2009529
 
7.4%
g 2009529
 
7.4%
h 2009529
 
7.4%
t 2009529
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27134502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 5695619
21.0%
a 5695619
21.0%
y 5695619
21.0%
N 2009529
 
7.4%
i 2009529
 
7.4%
g 2009529
 
7.4%
h 2009529
 
7.4%
t 2009529
 
7.4%

Duration_Seconds
Real number (ℝ)

Skewed 

Distinct74455
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26665.257
Minimum73
Maximum1.6877634 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.0 MiB
2024-11-05T00:33:20.660341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile1724
Q11890
median4490
Q37509
95-th percentile21600
Maximum1.6877634 × 108
Range1.6877627 × 108
Interquartile range (IQR)5619

Descriptive statistics

Standard deviation810737.55
Coefficient of variation (CV)30.404265
Kurtosis3659.5997
Mean26665.257
Median Absolute Deviation (MAD)2700
Skewness50.637291
Sum2.0607962 × 1011
Variance6.5729538 × 1011
MonotonicityNot monotonic
2024-11-05T00:33:20.733556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21600 369307
 
4.8%
1800 116096
 
1.5%
2700 70075
 
0.9%
4500 61374
 
0.8%
3600 61173
 
0.8%
14400 57317
 
0.7%
1785 56112
 
0.7%
1786 55955
 
0.7%
1787 55058
 
0.7%
1784 54369
 
0.7%
Other values (74445) 6771558
87.6%
ValueCountFrequency (%)
73 1
 
< 0.1%
115 1
 
< 0.1%
120 2
 
< 0.1%
150 3
 
< 0.1%
152 1
 
< 0.1%
180 16
< 0.1%
210 6
 
< 0.1%
221 1
 
< 0.1%
229 1
 
< 0.1%
240 14
< 0.1%
ValueCountFrequency (%)
168776340 2
< 0.1%
134184345 1
 
< 0.1%
134181332 3
< 0.1%
134179838 3
< 0.1%
134176830 2
< 0.1%
106135755 1
 
< 0.1%
100954757 1
 
< 0.1%
94755540 1
 
< 0.1%
94697995 1
 
< 0.1%
94697990 1
 
< 0.1%

Interactions

2024-11-05T00:32:42.900800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:07.206273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:12.995863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:19.237218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:25.494492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:31.539763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:37.362961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:43.743837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:08.029495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:13.868406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:20.094660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:26.367477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:32.375665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:38.130208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:44.598204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:08.853720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:14.765100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:20.944478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:27.230263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:33.214291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:38.889836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:45.459025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:09.675112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:15.698349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:21.822698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:28.078564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:34.074805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:39.668289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:46.311059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:10.499318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:16.598414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:22.780232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:28.908735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:34.910082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:40.439197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:47.126022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:11.292355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:17.455482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:23.674374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:29.740734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:35.719894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:41.231688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:47.882708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:12.134751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:18.353327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:24.591793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:30.633500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:36.575655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T00:32:42.040345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-05T00:33:20.794166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AmenityCivil_TwilightCrossingDistance(mi)Duration_SecondsGive_WayHumidity(%)JunctionNo_ExitPressure(in)RailwaySeverityStopTemperature(F)Traffic_CalmingTraffic_SignalVisibility(mi)Wind_DirectionWind_Speed(mph)
Amenity1.0000.0070.1490.0010.0020.0060.0150.0260.0140.0230.0500.0390.0340.0110.0240.1060.0020.0230.000
Civil_Twilight0.0071.0000.0400.0030.0070.0050.2420.0160.0050.0300.0000.0550.0010.2640.0000.0450.0160.1810.001
Crossing0.1490.0401.0000.0040.0080.0580.0350.0880.0610.0350.1780.1210.1160.0510.0380.4770.0070.0520.001
Distance(mi)0.0010.0030.0041.0000.4040.000-0.0190.0010.000-0.1250.0000.0060.001-0.0580.0010.004-0.0090.001-0.005
Duration_Seconds0.0020.0070.0080.4041.0000.001-0.0220.0080.001-0.1330.0010.0090.004-0.0250.0000.0080.0110.005-0.038
Give_Way0.0060.0050.0580.0000.0011.0000.0050.0090.0060.0080.0030.0090.0310.0040.0030.0720.0070.0100.000
Humidity(%)0.0150.2420.035-0.019-0.0220.0051.0000.0100.0090.0570.0060.0280.026-0.3310.0050.020-0.4650.088-0.198
Junction0.0260.0160.0880.0010.0080.0090.0101.0000.0040.0270.0090.0530.0370.0210.0050.1050.0040.0300.000
No_Exit0.0140.0050.0610.0000.0010.0060.0090.0041.0000.0070.0040.0120.0260.0060.0120.0300.0070.0060.000
Pressure(in)0.0230.0300.035-0.125-0.1330.0080.0570.0270.0071.0000.0160.0460.002-0.0040.0050.0370.0670.089-0.000
Railway0.0500.0000.1780.0000.0010.0030.0060.0090.0040.0161.0000.0120.0070.0100.0050.0580.0030.0080.000
Severity0.0390.0550.1210.0060.0090.0090.0280.0530.0120.0460.0121.0000.0600.0350.0060.1210.0120.1090.001
Stop0.0340.0010.1160.0010.0040.0310.0260.0370.0260.0020.0070.0601.0000.0150.0260.0480.0010.0090.000
Temperature(F)0.0110.2640.051-0.058-0.0250.004-0.3310.0210.006-0.0040.0100.0350.0151.0000.0050.0440.2240.0770.088
Traffic_Calming0.0240.0000.0380.0010.0000.0030.0050.0050.0120.0050.0050.0060.0260.0051.0000.0120.0010.0060.000
Traffic_Signal0.1060.0450.4770.0040.0080.0720.0200.1050.0300.0370.0580.1210.0480.0440.0121.0000.0030.0750.001
Visibility(mi)0.0020.0160.007-0.0090.0110.007-0.4650.0040.0070.0670.0030.0120.0010.2240.0010.0031.0000.0110.055
Wind_Direction0.0230.1810.0520.0010.0050.0100.0880.0300.0060.0890.0080.1090.0090.0770.0060.0750.0111.0000.002
Wind_Speed(mph)0.0000.0010.001-0.005-0.0380.000-0.1980.0000.000-0.0000.0000.0010.0000.0880.0000.0010.0550.0021.000

Missing values

2024-11-05T00:32:48.936582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-05T00:32:54.415099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-05T00:33:10.113523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SeverityDistance(mi)StreetZipcodeTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_Seconds
030.01I-70 E4542436.991.029.6810.0CalmNaNLight RainFalseFalseFalseFalseFalseFalseFalseFalseFalseNight18840.0
120.01Brice Rd43068-340237.9100.029.6510.0CalmNaNLight RainFalseFalseFalseFalseFalseFalseFalseFalseFalseNight1800.0
220.01State Route 324517636.0100.029.6710.0SW3.5OvercastFalseFalseFalseFalseFalseFalseFalseFalseTrueNight1800.0
330.01I-75 S4541735.196.029.649.0SW4.6Mostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
420.01Miamisburg Centerville Rd4545936.089.029.656.0SW3.5Mostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseTrueDay1800.0
530.01Westerville Rd4308137.997.029.637.0SSW3.5Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
620.00N Woodward Ave45417-247634.0100.029.667.0WSW3.5OvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
730.01N Main St4540534.0100.029.667.0WSW3.5OvercastFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
820.00Notre Dame Ave45404-192333.399.029.675.0SW1.2Mostly CloudyFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
930.01Westerville Rd4308137.4100.029.623.0SSW4.6Light RainFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1800.0
SeverityDistance(mi)StreetZipcodeTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_Seconds
772838420.390I-10 E9170678.052.029.6910.0VAR6.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1723.0
772838520.000CA-60 E9255588.032.028.2010.0WNW10.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1703.0
772838620.189El Camino Real N9300373.068.029.7610.0W9.0FairFalseFalseFalseTrueFalseFalseFalseFalseFalseDay1703.0
772838720.443Santa Ana Fwy S9278075.060.029.7410.0SSW9.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1711.0
772838820.000Golden State Fwy N9133181.048.028.7810.0ESE6.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1711.0
772838920.543Pomona Fwy E9250186.040.028.9210.0W13.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1716.0
772839020.338I-8 W9210870.073.029.3910.0SW6.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1613.0
772839120.561Garden Grove Fwy9286673.064.029.7410.0SSW10.0Partly CloudyFalseFalseFalseTrueFalseFalseFalseFalseFalseDay1708.0
772839220.772San Diego Fwy S9023071.081.029.6210.0SW8.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1761.0
772839320.537CA-210 W9234679.047.028.637.0SW7.0FairFalseFalseFalseFalseFalseFalseFalseFalseFalseDay1765.0

Duplicate rows

Most frequently occurring

SeverityDistance(mi)StreetZipcodeTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_Seconds# duplicates
9201520.521Foothill Fwy W91106NaNNaNNaNNaNNaNNaNNaNFalseFalseFalseFalseFalseFalseFalseFalseFalseDay21600.036
7811720.296US-169 S66203NaNNaNNaNNaNNorthNaNNaNFalseFalseFalseFalseFalseFalseFalseFalseFalseDay21600.034
8635220.421I-35 N66103NaNNaNNaNNaNNorthNaNNaNFalseFalseFalseTrueFalseFalseFalseFalseFalseDay21600.031
6732420.181I-35 N66203NaNNaNNaNNaNNorthNaNNaNFalseFalseFalseFalseFalseFalseFalseFalseFalseDay21600.029
7223020.229Lee Hwy22031-231846.668.030.3610.0WSW5.8ClearFalseTrueFalseFalseFalseFalseFalseFalseFalseDay3389400.028
8526220.403I-35 S66203NaNNaNNaNNaNNorthNaNNaNFalseFalseFalseTrueFalseFalseFalseFalseFalseDay21600.026
9215020.524CA-134 E91101NaNNaNNaNNaNNaNNaNNaNFalseFalseFalseFalseFalseFalseFalseFalseFalseDay21600.026
8550420.407US-169 S66203NaNNaNNaNNaNNorthNaNNaNFalseFalseFalseTrueFalseFalseFalseFalseFalseDay21600.024
5368020.095N Highway 9797703NaNNaNNaNNaNNaNNaNNaNFalseFalseFalseTrueFalseFalseFalseFalseFalseNight14400.019
13312523.768Richmond-San Rafael BrgNaNNaNNaNNaNNaNNaNNaNNaNFalseFalseFalseFalseFalseFalseFalseFalseFalseDay21600.019